Fully Funded PhD Programs: Scalable Digital Twins with Advanced Machine Learning Integration for Offshore Wind Turbine Operation and Maintenance Cranfield University 🇬🇧

Cranfield University’s fully supported Ph. D.s can change the energy business! Are you into renewable energy and cutting-edge tech? This unique opportunity optimises offshore wind turbine operations using digital twins and advanced machine learning.

Help us shape sustainable energy.

• PhD Studentship: Scalable Digital Twins with Advanced Machine Learning for Offshore Wind Turbine Maintenance

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Cranfield University PhD Programs Overview 2024

• Institute: Cranfield University

• Location: Cranfield

• Funding for UK Students: The funding amount is £20,000 tax-free, plus fees for three years.

• Hours: Full-Time

• Posted: July 11 2024; Closed: August 28 2024

• UK student funding: A three-year bursary of up to £20,000 tax-free plus fees will be granted.

Supervisor: Dr. Ravi Pandit

Breaking News: Fully funded PhD in UK for international students 2024-25

Cranfield University PhD Programs Overview 2024

Cranfield University, Semtronics UK, and international academic partners are offering a PhD studentship to improve offshore wind turbine efficiency and dependability using digital twin and machine learning technology.

This project will identify knowledge gaps in digital twin and machine learning models and use public and industry datasets to construct scalable machine learning-based digital twin models to improve performance, decision-making, and cost. A three-year tax-free bursary of up to £20,000 plus fees will be awarded.

While digital twins are growing in other sectors, their use in wind turbines must be explored due to crucial knowledge gaps in advanced machine learning integration. Comprehensive wind farm digital twin research incorporating multiple data sources is scarce. Current predictive maintenance uses machine learning without advanced techniques like deep learning for complex data.

Real-time data processing for operational modifications is lacking. Synthetic data for digital twin calibration needs to be developed more, especially in real-world situations. Last, most models are turbine-specific, therefore challenging to scale and adapt to varied wind farm settings. Addressing these gaps improves wind turbine performance and lowers costs.

This PhD study uses digital twin technology and advanced machine learning to fill wind energy optimization gaps. It entails creating a holistic digital twin model using wind farm data to use and improve machine learning technologies like deep learning for predictive maintenance.

The project prioritizes real-time data processing for dynamic operational modifications and validates the model with synthetic and accurate sensor data to ensure robust performance in different settings. The initiative will also examine the pros and cons of utilizing digital twins to optimize wind farm performance.

Fully Funded PhD Programs Cranfield University Entry Requirements

A 1st or 2.1 UK degree in electrical engineering, energy, or computer science is required. The ideal candidate has electrical, computer, and wind turbine programming experience.

The candidate should be self-motivated, have good communication skills for stakeholder interactions, and be effective at industrial research.

UK citizens must apply.

• Funding, sponsorship

Cranfield University and EPSRC sponsors.

• Last day: 28/08/2024 

• Apply Now CLICK HERE

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